{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8bca284b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1797, 64) (1797,)\n",
      "[[ 0.  0.  5. ...  0.  0.  0.]\n",
      " [ 0.  0.  0. ... 10.  0.  0.]\n",
      " [ 0.  0.  0. ... 16.  9.  0.]\n",
      " ...\n",
      " [ 0.  0.  1. ...  6.  0.  0.]\n",
      " [ 0.  0.  2. ... 12.  0.  0.]\n",
      " [ 0.  0. 10. ... 12.  1.  0.]] [0 1 2 ... 8 9 8]\n",
      "(8, 8)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "[[ 0.         -0.33501649 -0.04308102 ... -1.14664746 -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649 -1.09493684 ...  0.54856067 -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649 -1.09493684 ...  1.56568555  1.6951369\n",
      "  -0.19600752]\n",
      " ...\n",
      " [ 0.         -0.33501649 -0.88456568 ... -0.12952258 -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649 -0.67419451 ...  0.8876023  -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649  1.00877481 ...  0.8876023  -0.26113572\n",
      "  -0.19600752]]\n",
      "[[ 1.91421366 -0.95450157 -3.94603482 ... -0.01797902  0.04795038\n",
      "   0.01912424]\n",
      " [ 0.58898033  0.9246358   3.92475494 ... -1.14415907  0.03774402\n",
      "   0.37167996]\n",
      " [ 1.30203906 -0.31718883  3.02333293 ...  0.48730406 -1.35695937\n",
      "  -0.10701564]\n",
      " ...\n",
      " [ 1.02259599 -0.14791087  2.46997365 ... -0.15253558  0.0509132\n",
      "   0.59550456]\n",
      " [ 1.07605522 -0.38090625 -2.45548693 ... -0.44673737 -0.46840846\n",
      "   0.16065193]\n",
      " [-1.25770233 -2.22759088  0.28362789 ...  0.43650024 -0.92270619\n",
      "  -0.06156133]]\n",
      "(1797, 34)\n",
      "直接比对预测值与真实值：\n",
      " [ True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      " False  True  True  True  True False  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True False\n",
      "  True  True  True  True False  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True False  True  True  True  True  True  True\n",
      "  True  True  True False  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True False\n",
      "  True False  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True False  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True False  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True False\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True]\n",
      "准确率为：\n",
      " 0.9755555555555555\n",
      "this is a test file!\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[52  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 42  0  0  0  0  0  0  0  0]\n",
      " [ 0  0 50  0  0  0  0  0  1  0]\n",
      " [ 0  0  0 39  0  0  0  0  1  0]\n",
      " [ 0  0  0  0 48  0  0  2  1  0]\n",
      " [ 0  0  0  0  0 51  0  0  0  0]\n",
      " [ 0  0  0  0  0  0 45  0  0  0]\n",
      " [ 0  0  0  0  1  0  0 43  0  0]\n",
      " [ 0  2  0  0  0  0  0  0 37  0]\n",
      " [ 0  0  0  2  0  1  0  0  0 32]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        52\n",
      "           1       0.95      1.00      0.98        42\n",
      "           2       1.00      0.98      0.99        51\n",
      "           3       0.95      0.97      0.96        40\n",
      "           4       0.98      0.94      0.96        51\n",
      "           5       0.98      1.00      0.99        51\n",
      "           6       1.00      1.00      1.00        45\n",
      "           7       0.96      0.98      0.97        44\n",
      "           8       0.93      0.95      0.94        39\n",
      "           9       1.00      0.91      0.96        35\n",
      "\n",
      "    accuracy                           0.98       450\n",
      "   macro avg       0.97      0.97      0.97       450\n",
      "weighted avg       0.98      0.98      0.98       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from common.utils import plot_learning_curve\n",
    "import seaborn as sn\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_digits\n",
    "\n",
    "\n",
    "digits = load_digits()\n",
    "X = digits.data\n",
    "y = digits.target\n",
    "\n",
    "\n",
    "def show(image):\n",
    "    test = image.reshape((8, 8))  # 从一维变为二维，这样才能被显示\n",
    "    print(test.shape)  # 查看是否是二维数组\n",
    "    # print(test)\n",
    "    plt.imshow(test)  # 显示灰色图像\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def standard_demo(data):\n",
    "    transfer = StandardScaler()\n",
    "    data_new = transfer.fit_transform(data)\n",
    "    print(data_new)\n",
    "    return data_new\n",
    "\n",
    "\n",
    "def pca_demo(data):\n",
    "    transfer = PCA(n_components=0.92)\n",
    "    data_new = transfer.fit_transform(data)\n",
    "    print(data_new)\n",
    "    return data_new\n",
    "\n",
    "\n",
    "def knn_demo(data, label):\n",
    "    # 划分数据集\n",
    "    X_train, X_test, y_train, y_test = train_test_split(data, label, random_state=6)\n",
    "    # 训练模型\n",
    "    estimate = KNeighborsClassifier(n_neighbors=10)\n",
    "    estimate.fit(X_train, y_train)  # 模型构建好了\n",
    "    # 模型评估的两种方法：1：直接比对预测值与真实值；\n",
    "    y_predict = estimate.predict(X_test)\n",
    "    print(\"直接比对预测值与真实值：\\n\", y_test == y_predict)\n",
    "    # 2：计算准确率\n",
    "    score = estimate.score(X_test, y_test)\n",
    "    print(\"准确率为：\\n\", score)\n",
    "    # 绘制学习曲线!!!!\n",
    "    cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)  # 10折交叉验证\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(10, 6), dpi=144)\n",
    "    plot_learning_curve(ax, estimate, \"Learn Curve\",\n",
    "                        X_train, y_train, ylim=(0.0, 1.01), cv=cv)\n",
    "    plt.show()\n",
    "    # 混淆矩阵\n",
    "    cm = confusion_matrix(y_test, y_predict)\n",
    "    print(cm)\n",
    "    # 可视化显示混淆矩阵\n",
    "    # annot = True 显示数字 ，fmt参数不使用科学计数法进行显示\n",
    "    ax = sn.heatmap(cm, annot=True, fmt='.20g')\n",
    "    ax.set_title('confusion matrix')  # 标题\n",
    "    ax.set_xlabel('predict')  # x轴\n",
    "    ax.set_ylabel('true')  # y轴\n",
    "    plt.show()\n",
    "    # 计算精确率与召回率\n",
    "    report = classification_report(y_test, y_predict, labels=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
    "                                   target_names=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])\n",
    "    print(report)\n",
    "\n",
    "\n",
    "# 按间距中的绿色按钮以运行脚本。\n",
    "if __name__ == '__main__':\n",
    "    # 查看数据集，以图片显示\n",
    "    print(X.shape, y.shape)\n",
    "    print(X, y)\n",
    "    show(X[1795]*15)\n",
    "    print(y[1795])\n",
    "    # 数据集预处理（标准化、特征降维）\n",
    "    X_new = standard_demo(X)\n",
    "    X_new = pca_demo(X_new)\n",
    "    print(X_new.shape)  # 从64维降到了40维\n",
    "    # 机器学习建模\n",
    "    # 调参\n",
    "    # 模型评估\n",
    "    # 学习曲线\n",
    "    knn_demo(X_new, y)  # 数据已经准备好了\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77f740e1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
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